In 2018 alone, an estimated 20,000 new acute myeloid leukemia (AML) patients were diagnosed, in the United States, and over 10,000 of them are expected to die from the disease. AML is primarily diagnosed among the elderly (median 68 years old at diagnosis). Prognoses have significantly improved for younger patients, but in patients older than 60 years old as much as 70% of patients will die within a year of diagnosis. In this study, we conducted stratified computational meta-analysis of 2,213 acute myeloid leukemia patients compared to 548 healthy individuals, using curated publicly available data. We carried out analysis of variance of normalized batch corrected data, including considerations for disease, age, tissue and sex. We identified 974 differentially expressed probe sets and 4 significant pathways associated with AML. Additionally, we identified 70 sex- and 375 age-related probe set expression signatures relevant to AML. Finally, we used a machine learning model (KNN model) to classify AML patients compared to healthy individuals with 90+% achieved accuracy. Overall our findings provide a new reanalysis of public datasets, that enabled the identification of potential new gene sets relevant to AML that can potentially be used in future experiments and possible stratified disease diagnostics.
Acute myeloid leukemia induces protumoral p16INK4a-driven senescence in the bone marrow microenvironment